Main biochemical identifications of
Abstract
Staphylococci are mainly found on the skin or in the nose. These bacteria are typically friendly, causing no harm to healthy individuals or resulting in only minor issues that can go away on their own. However, under certain circumstances, staphylococcal bacteria could invade the bloodstream, affect the entire body, and lead to life-threatening problems like septic shock. In addition, antibiotic-resistant Staphylococcus is another issue because of its difficulty in the treatment of infections, such as the notorious methicillin-resistant Staphylococcus aureus (MRSA) which is resistant to most of the currently known antibiotics. Therefore, rapid and accurate diagnosis of Staphylococcus and characterization of the antibiotic resistance profiles are essential in clinical settings for efficient prevention, control, and treatment of the bacteria. This chapter highlights recent advances in the diagnosis of Staphylococci in clinical settings with a focus on the advanced technique of surface-enhanced Raman spectroscopy (SERS), which will provide a framework for the real-world applications of novel diagnostic techniques in medical laboratories via bench-top instruments and at the bedside through point-of-care devices.
Keywords
- Staphylococcus
- rapid diagnosis
- mass spectrometry
- Raman spectrometry
- machine-learning algorithm
1. Introduction
Bacteria belonging to the genus
2. Clinical significance of Staphylococcus infections
2.1 Staphylococcal species
The genus
2.2 Staphylococcal biological properties
2.3 Distribution and epidemiology
2.4 Staphylococcal infections
Coagulase-negative staphylococci (CNS) represented by
3. Traditional identification of staphylococcal bacteria
3.1 Microscopic inspection and culture
Microscopic inspection is based on performing morphological tests on colonies. Clinical specimens were smeared, Gram-stained, and the morphology was observed under a microscope.
3.2 Staphylococcal biochemical identification
The majority of staphylococcal oxidase tests are negative.
Characteristic | |||||||
---|---|---|---|---|---|---|---|
Colony pigment | White/yellow | Yellow/milk white | White/yellow | White | White | Colorless/gray | ND |
G + C(%) | 30–39 | 66–75 | 34–42 | 35–40 | 38–45 | 34–46 | 39–52 |
Strictly aerobic | − | + | − | − | ± | − | + |
Quadruple arrangement | d | + | − | + | d | − | + |
Motility | − | − | d | − | − | − | + |
6.5% NaCl | + | + | + | + | + | d | + |
Catalase | + | + | − | − | + | − | + |
Oxidase (modified method) | − | + | − | − | + | − | ND |
Glucose anaerobic acid production | d | − | + | (±) | − | + | − |
Glycerol aerobic acid production | + | − | d | ND | ND | d | − |
Benzidine test | + | + | − | − | ND | − | + |
Erythromycin (0.4 ug/ml) | R | S | R | ND | R | S | ND |
Bacitracin (0.04 U/disk) | R | S | R | S | R | d | ND |
Furazolidone (100 ug/disk) | S | R | S | S | S | S | S |
Glucolysin (200 ug/disk) | S | R | R | R | R | R | R |
+ | + | + | + | + | + | + | + | − | + | + | − | |
− | − | (+) | (+) | + | + | − | − | − | + | + | ND | |
+ | − | + | + | − | d | − | + | − | + | + | − | |
+ | − | (+) | + | d | + | d | + | − | + | + | + | |
d | d | + | + | (+) | − | (+) | + | − | − | − | + | |
− | − | + | + | (+) | − | + | + | − | + | + | + | |
d | − | + | + | (+) | + | − | + | − | + | + | ND | |
+ | − | (+) | + | + | + | − | + | − | − | + | ND | |
− | − | + | + | + | + | − | + | − | (±) | + | ND | |
− | d | (d) | + | (±) | − | d | + | + | − | d | − | |
d | d | (d) | + | (±) | − | + | + | + | − | d | − | |
+ | − | ND | + | + | + | − | + | ND | + | + | + | |
− | − | + | + | (d) | − | − | + | − | − | + | − | |
− | d | + | + | + | − | − | − | + | + | + | + | |
ND | ND | ND | − | − | −− | − | − | ND | − | + | + | |
− | d | + | + | − | − | − | − | + | + | − | + | |
− | + | d | + | − | − | + | − | − | d | (±) | + | |
+ | d | − | + | + | − | − | − | d | + | + | + | |
− | − | + | + | + | − | (+) | − | − | − | − | − | |
ND | + | + | + | + | ND | ND | − | ND | d | − | d | |
ND | + | + | + | − | ND | ND | − | ND | + | + | + | |
ND | + | − | + | − | ND | + | − | ND | + | + | ND | |
− | − | − | + | − | + | − | + | − | (d) | (d) | + | |
− | − | − | + | − | + | − | + | − | (d) | (d) | + | |
ND | + | ND | + | ND | ND | + | − | ND | + | + | + | |
(d) | + | d | + | + | − | − | − | + | d | + | + | |
+ | d | (+) | + | (+) | − | − | + | − | − | − | + | |
+ | d | (+) | + | − | − | − | + | − | − | − | + | |
− | + | + | + | − | − | − | + | − | − | − | − | |
d | d | + | + | (d) | − | − | + | − | − | − | − | |
− | d | − | + | − | − | − | + | − | − | − | − | |
− | − | − | + | − | − | − | + | − | − | − | + | |
+ | − | + | + | (d) | − | − | + | − | − | (d) | + | |
− | − | (+) | + | (d) | − | − | + | − | − | − | − | |
− | (d) | (+) | + | (d) | − | − | + | − | − | − | (d) | |
d | − | d | + | (d) | − | − | + | − | − | − | − | |
+ | d | (+) | + | (d) | − | − | + | − | − | + | d | |
+ | d | d | + | − | − | − | + | − | − | d | d | |
d | − | (+) | + | (d) | − | − | + | − | − | (+) | d | |
− | − | + | + | + | − | − | − | − | d | + | + | |
− | + | − | − | + | − | + | + | − | d | + | + | |
− | + | − | + | d | − | d | + | ND | − | + | + | |
− | + | d | d | + | − | − | − | − | d | (+) | + | |
− | + | d | d | d | − | − | − | − | d | + | (+) | |
− | + | − | d | d | + | − | + | ND | d | + | (+) | |
− | + | + | + | d | − | + | − | d | + | (±) | + | |
− | − | d | d | d | − | − | − | − | (+) | − | (+) | |
− | + | + | + | d | − | − | − | ND | + | + | + | |
− | − | − | − | d | − | − | + | − | − | (d) | − | |
− | + | − | − | d | − | + | + | − | + | (+) | − | |
− | + | − | d | d | d | + | + | − | d | + | + | |
− | + | + | + | + | − | − | − | − | + | (d) | − |
3.3 Antibiotic resistance
The conventional approaches for antibiotic susceptibility testing of
Commercial detection systems for the broth dilution method for drug susceptibility mainly include bioMérieux (http://www.biomerieuxusa.com), Siemens Health-care Diagnostics (http://www.siemens.com), Becton Dickinson Diagnostics (http://www.bd.com) and Thermo Scientific (http://www.thermoscientific.com).
Species | Resistant phenotype | Test method | Medium | Drug | Incubation conditions | Results | Quality control | Whether or not to be confirmed |
---|---|---|---|---|---|---|---|---|
Oxacillin Resistancea | Oxacillin-Salt Agar Screen | MHA + 4%NaCl | 6 ug/ml Oxacillin | 33–35°C, ambient air, 24 h | ≥1 colony | S. aureus ATCC®29213 S. aureus ATCC® 43300 | No | |
Cefoxitin Broth Microdilution | CAMHB | Cefoxitin | 33–35°C, ambient air, 16–20 h | >4 ug/ml = | S. aureus ATCC®25,923 | No | ||
Cefoxitin Disk Diffusion | MHA | 30 μg Cefoxitin disk | 35± 2 °C, ambient air, 16–18 h | ≤21 mm = | S. aureus ATCC®25,923 | No | ||
Vancomycin MIC ≥ 8 ug/ml | BHI agar dilution | BHI agar | 6 ug/ml Vancomycin | 35 ± 2 °C, aerobic, 24 h | ≥1 colony, presumptive susceptibility reduced | Enterococcus faecalis ATCC® 29,212 | Yes | |
Disc Diffusion | MHA | 30 ug Vancomycin disk | 35 ± 2°C, 16–18 h | 6 mm, presumptive resistant | S. aureus ATCC®25,923 | Yes | ||
Inducible Clindamycin Resistance | Clindamycin-Erythromycin Broth Microdilution | CAMHB | 4 ug Erythromycin and 0.5 ug Clindamycin in the same well | 35 ± 2 °C,ambient air, 18–24 h | Any growth = positive, no growth = non-inducible clindamycin resistance | S. aureus ATCC®BAA-976, S. aureus ATCC®BAA-977 | No | |
D test (Disc Diffusion) | MHA or BAP | 15 ug Erythromycin disk and 2 fug Clindamycin disk are placed 15–26 mm apart | 35 ± 2 °C, ambient air, 16–18 h | zone edge appears “truncated” (similar to the English letter D) = positive; blurred zone edge (beach-like) = Clindamycin resistance | S. aureus ATCC®BAA-976, S. aureus ATCC®BAA-977 | No | ||
High-Level Mupirocin Resistance | Broth Microdilution | CAMHB | 256 ug/ml Mupirocin | 35 ± 2°C, ambient air, 24 h | grow = | S. aureus ATCC®29,213, S. aureus ATCC®BAA-1708 | No | |
Disc Diffusion | MHA | 200 ug Mupirocin disk | 35 ± 2 °C, ambient air, 24 h | no inhibition zone = | S. aureus ATCC®25,923, S. aureus ATCC®BAA-1708 | No | ||
CoNS | Cefoxitin Disk Diffusion | MHA | 30 μg Cefoxitin disk | 33–35°C, ambient air, 24 h | ≤24 mm = | S. aureus ATCC® 43,300 | No | |
Inducible Clindamycin Resistance | Clindamycin-Erythromycin Broth Microdilution | CAMHB | 4 ug Erythromycin and 0.5 ug Clindamycin in the same well | 35 ± 2 °C, ambient air, 18–24 h | Any growth = positive, no growth = non-inducible clindamycin resistance | S. aureus ATCC®BAA-976, S. aureus ATCC®BAA-977 | No | |
β-Lactamase Production | Disk diffusion (Penicillin zone-edge test) | MHA | 10 units penicillin disk | 35 ± 2 °C, ambient air, 16–20 h | Sharp zone edge = | S. aureus ATCC®25,923 | No | |
Nitrocefin-based test | N/A | N/A | <1 h or according to the manufacturer’s instructions for use | From yellow to red or pink = β-lactamase positive | S. aureus ATCC®29,213 S. aureus ATCC® 25,923 | Yes | ||
D test (Disc Diffusion) | MHA or BAP | 15 ug Erythromycin disk and 2 ug Clindamycin disk are placed 15–26 mm apart | 35 ± 2 °C, ambient air, 16–18 h | zone edge appears “truncated” (similar to the English letter D) = positive; blurred zone edge (beach-like) = clindamycin resistance | S. aureus ATCC®BAA-976, S. aureus ATCC®BAA-977 | No | ||
β-lactamase production | Nitrocefin-based test | N/A | N/A | <1 h or according to the manufacturer’s instructions for use | From yellow to red or pink = β-lactamase positive | S. aureus ATCC®29,213 S. aureus ATCC® 25,923 | Yes |
The cefoxitin disk diffusion assay of
4. Rapid diagnosis of Staphylococcal infections
4.1 PCR and its derived technologies
4.1.1 Polymerase chain reaction (PCR)
Polymerase chain reaction (PCR) is the most extensively used nucleic acid amplification method for bacterial serotyping and confirmation. RT-PCR (Real-time quantitative PCR) has high sensitivity, high specificity, low pollution, and a high degree of automation [38]. Its reaction is monitored in real-time and can quantitatively detect target genes. The detection time of clinical samples can even be shortened to 1 h. Recent literature reports show that RT-PCR technology is currently the most accurate, reproducible and internationally recognized standard method for the quantitative and qualitative detection of nucleic acid molecules. For example, Okolie et al. [39] simultaneously detected marker genes of Coagulase-negative
4.1.2 Isothermal nucleic acid amplification technology
Classical nucleic acid amplification technology has multiple thermal cycling steps, requires strict laboratory conditions, and relies on the use of high-precision instruments that are difficult to miniaturize. The isothermal amplification technology can perform accurate and rapid analysis on site, and is more suitable for integration into miniaturized systems [45]. Loop-mediated isothermal amplification (LAMP) technology was created by Notomi et al. in 2000 [46]. It is a nucleic acid amplification technology that can perform rapid, specific and sensitive detection of target sequences under isothermal conditions. Yin et al. [47] utilize LAMP technology combined with lateral flow assay (LFA) to simultaneously detect
4.2 Immunoassay
Immunology-based rapid detection technologies for microorganisms include Immune Fluorescence Assay (IFA), Enzyme-linked Immunosorbent Assay (ELISA), Chemiluminescence Immunoassay (CLIA), Radio Immunoassay (RIA), Immunomagnetic Separation (IMS), and Immune Colloidal Gold (ICG) technique, etc. Among them, IMS is a technology that uses the magnetic responsiveness of the magnetic beads to enrich and separate the target substances by coating the recognition substances such as antigens and antibodies on the superparamagnetic nanomagnetic beads with a specific particle size range. The technical operation is simple and fast, with high specificity and sensitivity. Currently, it has been extensively used in protein and nucleic acid purification, cell separation and pathogen detection, such as Multiple Polymerase Chain Reaction (MPCR), Recombinase Polymerase Amplification (RPA), and Loop-Mediated Isothermal Amplification (LAMP). Zhou et al. [50] use avidin-labeled magnetic beads and biotin-labeled SPA monoclonal antibodies to prepare immunomagnetic beads to enrich
4.3 Mass spectrometry
The molecular weight and structure of different biomolecules, such as proteins, nucleic acids, and polysaccharides, can be analyzed using matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) technology. The basic principle behind matrix-assisted laser desorption is as follows: after the matrix and the sample form an eutectic, the matrix and the sample absorb laser energy to desorb the sample, and charge transfer between the matrix and the sample occurs to ionize the sample molecules. The mass-to-charge ratio of ions is proportional, and the mass-to-charge ratio can be measured according to the flight time to the detector, and a characteristic fingerprint can be obtained through software processing [54]. MALDI-TOF MS technology has the characteristics of rapidity, accuracy, sensitivity and automation [55], and gradually occupies an important position in the identification of microbiology laboratories [56]. Rychert et al. [57] conducted a multicenter study on Gram-positive aerobic bacteria, and the results showed that in 1146 Gram-positive bacteria samples, the accuracy rate at the species level was 92.8%, and the accuracy at the genus level could reach 95.5%. The time required for MALDI-TOF MS to obtain identification results has been shortened from 5 to 48 h or even longer via traditional biochemical methods to less than 6 min per sample, and the cost of reagents for single-sample detection has been reduced to less than 1/4 of traditional methods. The overall identification accuracy of MALDI-TOF MS is >90%, which is higher than that of conventional methods (80–85%); in addition, MALDI-TOF MS is easy to operate, which significantly shortens the time for professional and technical training of personnel [58, 59]. MALDI-TOF MS can also be used to analyze the antibiotic resistance of bacteria. The advantages of MALDI-TOF MS are good specificity and short experimental time as compared with conventional antibiotic susceptibility testing (AST) [60, 61]. MOLDI-TOF MS can also quickly differentiate between MRSA and MSSA [62, 63]. The most essential characteristic peaks for distinguishing MRSA and methicillin-sensitive
4.4 Genome sequencing
In 1977, Sanger et al. [65] invented the dideoxyribonucleotide end termination method, and Maxam and Gilbert [66] developed the chemical degradation method, which marked the birth of the next generation of sequencing technology. Sanger sequencing is the standard technology and its length can be up to 1000 bp and the accuracy is almost 100%, but it has the disadvantages of low throughput, high cost, and long time. Next-generation sequencing (NGS) came into being. Next-generation sequencing platforms mainly include the Roche 454 sequencing platform based on microemulsion PCR and pyrosequencing technology, the Illumina sequencing platform based on bridge PCR and fluorescent reversible terminator sequencing-by-synthesis, the SOLID sequencing platform based on microemulsion PCR and oligonucleotide ligation sequencing, and the Ion Torrent PGM and Proton semiconductor sequencing platforms [67].
In 2014, Wilson et al. [68] reported the world’s first case of an infectious disease diagnosed by next-generation sequencing technology. Since then, NGS technology has been gradually recognized and promoted, providing ideas for the diagnosis of unknown pathogens in clinical practice [69] NGS is the most widely used method for high-throughput, massively parallel sequencing of thousands to billions of DNA fragments simultaneously [70]. The third-generation sequencing technology is divided into single-molecule real-time (SMRT) sequencing and nanopore single-molecule sequencing according to different sequencing principles. Gene sequencing can obtain the genomic information of pure colonies and the genomic information of mixed specimens so that highly related lineages can be distinguished with the resolution and precision that other methods lack. Gene sequencing can obtain nearly complete bacterial DNA information, including species, drug-resistance genes, virulence factors, mobile elements, etc. The molecular epidemiology and transmission mechanisms of strains are critical to understanding the occurrence and development of various diseases [71]. The widespread availability of genetic sequencing technology has enabled more detailed studies of MRSA transmission patterns, including analysis of past undocumented transmission and comprehensive, complicated strain evolution [72, 73, 74]. In addition, gene sequencing plays a significant role in the study of MRSA colonization and infection [75].
Moore et al. [76] demonstrated that Whole Genome Sequencing (WGS) has a high resolution for strains that other methods cannot distinguish in MRSA colonization and infection studies. WGS is a comprehensive method that analyzes the entire genomic DNA of a cell at once by using sequencing technology. At present, NGS technology still lacks unified laboratory testing operation specifications, and exogenous nucleic acid contamination will likely lead to false positive results, which will seriously affect clinical diagnosis. NGS can detect two or more non-pathogenic bacteria in the same specimen. The analysis may be because NGS has high sensitivity and the nucleic acid residues of non-specimen pathogens with low sequence numbers or dead pathogens are detected together, which is very likely to lead to misjudgment by clinicians, though NGS results lack recognized interpretation. However, the relationship between sequencing results and treatment is unclear, and drug-resistance genes are difficult to detect, so it still needs to be supplemented with drug susceptibility testing. In summary, NGS technology plays an essential role in identifying pathogens and guiding clinical treatment. With the continuous improvement of NGS detection platforms and the proposal of relevant interpretation, NGS technology will be widely used on standards to guide clinical diagnosis and treatment.
5. Raman spectroscopy in Staphylococcus identifications
5.1 Principles of Raman scattering effects
Raman scattering is an inelastic scattering phenomenon caused by light striking the surface of a material, revealed by Indian scientist Chandrasekhara Venkata Raman in 1928 [77]. When the molecules of the detected object interact with the incident light photons of the monochromatic beam, elastic and inelastic collisions can occur simultaneously. The scattering mode in which the optical frequency does not change is called Rayleigh scattering. The photon transfers energy to the molecule during an inelastic collision; after it changes direction, some of this energy is transferred to the molecule (Stokes scattering), or the vibration and rotational energy of the molecule is transferred to the photon (Anti-Stokes scattering), changing the frequency of the photon (Raman scattering) [78]. Because Raman scattering can reflect the molecular vibration and vibration-rotation energy level of substances, it is used in molecular structure analysis. However, due to the extremely low scattering efficiency of inelastic scattering, the scattered light intensity is one millionth to one billionth of the incident light intensity, which greatly limits the application of Raman spectroscopy in material analysis and detection, and surface-enhanced Raman spectroscopy was then discovered and developed.
5.2 Surface-enhanced Raman spectroscopy
In 1974, Fleischmann et al. [79] found that the pyridine molecules adsorbed on the rough silver electrode surface had a significant Raman scattering effect. In 1977, after extensive experimental research and theoretical calculation, Jeanmarie et al. [80] named this enhancement effect related to rough metal surfaces such as silver (Ag), gold (Au), and copper (Cu) as the surface-enhanced Raman scattering effect, and the corresponding technology was called surface-enhanced Raman spectroscopy (SERS). The Raman scattering signal of pyridine molecules adsorbed on the rough metal silver surface is enhanced by about 6 orders of magnitude compared to the Raman scattering signal of pyridine molecules in solution, which provides the possibility for the detection of biological macromolecules. The principle of SERS is explained mainly through two mechanisms: chemical enhancement and electromagnetic enhancement. The chemical mechanism (CM) describes the electronic interaction between substrates and adsorbed molecules and offers a small enhancement magnitude 102–103. The electromagnetic enhancement (EM) mechanism contributes by increasing the electromagnetic field near plasmonic structures caused by incident light excitation of a localized surface plasmon resonance (LSPR). Plasmonic nanomaterials are those in which incident electromagnetic radiation from light can coherently excite conduction electrons to oscillate collectively at metal/dielectric interfaces. The large SERS enhancement factor (EF) generated from EM contribution to plasmonic nanomaterials is in the magnitude of 1010–1014 [81] which is significant for the detection of single molecules [82]. Among them, electromagnetic enhancement receives more attention and acknowledges extensive research work. Label-free SERS detection technology has developed into a research hotspot in the field of microbiology due to its advantages of no need for too much preliminary preparation, non-invasive and short detection time, and excellent application prospects in bacterial detection.
5.3 SERS spectra of staphylococcal bacteria
The complex biological meaning and structural information contained in Raman spectra result from the vibrational and rotational frequencies of molecules in the sample. The vibration frequencies of biomolecules such as nucleic acids, proteins, lipids, and carbohydrates in bacteria are different, and they appear as unique peaks in Raman spectra. “Full biometric fingerprints” can be used as a basis for distinguishing different bacteria. Efrima et al. [83] used SERS for bacterial detection, distinguishing Gram-positive and Gram-negative bacteria through the difference in SERS profiles on the cell membrane surface. Since then, the application of SERS in bacterial detection, identification, and classification has received rapid attention. Rebrošová et al. [84] detected 54
In addition to achieving bacterial classification, SERS technology offers the potential to discriminate various bacterial species that belong to the same family. You et al. [89] used 30 cases of
5.4 Raman spectroscopy preprocessing
Raman spectral signals inevitably receive external interference during the acquisition process, such as the mechanical vibration of the instrument itself, cosmic noise, and autofluorescence to a certain extent, which prevents the rapid and accurate analysis of spectral data [95]. Therefore, before formal data analysis, the original Raman signal needs to be preprocessed to eliminate unfavorable factors in the analysis process. Preprocessing can be regarded as a key step in spectral data analysis and is mainly divided into spike removal, smoothing denoising, baseline correction, and vector normalization. For peak removal, when collecting Raman spectra, random, narrow and strong spectral signals appear in the spectral fingerprint due to the random appearance of electronic signals from cosmic particles on CCD or complementary metal-oxide-semiconductor detectors. The existence of spikes will mask other useful information to a great extent; therefore, spike removal is necessary. In general, spikes rarely appear at the same shift in the Raman spectrum of the same sample [96]. In this regard, we can judge whether there is a spike by visually inspecting and comparing the difference in abnormal intensity between different spectral curves [97]. In addition, setting the signal intensity threshold and deriving the spectral data can also achieve the purpose of removing spikes [98]. For the electronic noise composed of cosmic noise, flicker noise, and thermal noise, it will randomly appear in multiple positions of the spectral curve in an unpredictable form, which has a large impact on the quality of Raman spectroscopy data. Savitzky–Golay (S-G) filtering is one of the most commonly used preprocessing methods in the process of smoothing and denoising Raman spectra [99, 100]. This method can keep the shape and width of the signal unchanged while filtering the noise, so as to meet the processing requirements of Raman spectral data in different situations [101]. As one of the recognized best processing steps in Raman spectrum analysis preprocessing [96], baseline correction is used to deal with the continuous distortion caused by uncontrollable factors during Raman spectrum acquisition, such as removing substrate-related Raman signals [99] and fluorescence signals generated by the sample itself [102]. Commonly used methods are asymmetric weighted penalized least squares (arPLS) algorithm [103], adaptive iterative weighted penalized least squares (airPLS) algorithm and polynomial fitting [104]. Normalization is the last step of preprocessing [105]. It is used to deal with the situation of large signal strength caused by uneven sample distribution, laser power difference, experimental environment interference and other factors [104]. Vector normalization is one of the most commonly used normalization methods in Raman spectral analysis [97, 106], It is used to control the difference in Raman signal intensity levels by mapping the data to a range of 0 to 1 for processing [107]. It is worth noting that the order of preprocessing is not fixed and each step does not necessarily need to be performed. When applying to our own experimental data, we need to observe the interaction between each step of preprocessing, and choose the best combination of preprocessing according to the feedback between different preprocessing methods.
5.5 Machine learning analysis of SERS spectra
Data learning aims to convert Raman spectral signals into computer-recognizable abstract feature information. For previously preprocessed spectral data, we need to use more advanced methods based on machine learning algorithms. Machine Learning (ML) is a method of observing existing data, extracting the rules, and then applying them to unknown samples [98]. Traditional Raman spectrum classification and recognition usually use machine-learning algorithms to model and analyze, but the analysis process of this method is relatively complicated, and it needs to go through operations such as preprocessing and feature extraction. In recent years, deep learning has become a hot research topic. Deep learning is to learn features from large-scale raw datasets and to build predictive models directly. There are many deep learning algorithms, including convolutional neural networks (CNN), fully connected networks, and residual neural networks (ResNet), etc. It has decent performance in mining local features of data and extracting international training highlights [108], and its ability to classify and identify data far exceeds that of traditional multivariate statistical analysis algorithms. Wang et al. [109] prepared positively charged nano-silver-based SERS samples combined with the CNN algorithm for rapid identification of drug resistance in
6. Conclusion and perspectives
With the continuous development of science and technology, the detection methods of
Glossary | Abbreviations |
---|---|
MRSA | methicillin-resistant |
SERS | surface enhanced Raman spectroscopy |
CPS | coagulase-positive Staphylococcus |
CNS | coagulase-negative Staphylococcus |
SCVs | Small colony variants |
MSM | mannitol salt medium |
SPA | Staphylococcus Protein A |
SSSS | staphylococcal scalded skin syndrome |
TSS | toxic shock syndrome |
SFP | |
SEs | staphylococcal enterotoxins |
CLSI | Clinical and Laboratory Standards Institute |
EUCAST | European Committee for Antimicrobial Susceptibility Testing |
K-B | Kirby-Bauer |
MIC | minimum inhibitory concentration |
MHB | Mueller Hinton Broth |
MBC | minimal bactericidal concentration |
VISA | vancomycin-intermediate |
VRSA | vancomycin-resistant |
PCR | Polymerase chain reaction |
NF | necrotizing fasciitis |
LAMP | Loop-mediated isothermal amplification |
LFA | lateral flow assay |
SDA | Strand displacement amplification |
RPA | recombinase polymerase amplification |
RCA | rolling circle amplification |
SAT | simultaneous amplification and testing |
IFA | immune fluorescence assay |
ELISA | enzyme-linked immunosorbent assay |
CLIA | chemiluminescence immunoassay |
RIA | radioimmunoassay |
IMS | immunomagnetic separation |
ICG | immune colloidal gold |
SEA | staphylococcal enterotoxin A |
MSPE | microscale solid phase extraction |
FITC | fluorescein isothiocyanate |
MALDI-TOF MS | matrix-assisted laser desorption ionization-time of flight mass spectrometry |
AST | antibiotic susceptibility testing |
MSSA | methicillin-sensitive |
NGS | next-generation sequencing |
SMRT | single-molecule real-time |
CM | chemical mechanism |
EM | electromagnetic enhancement |
LSPR | localized surface plasmon resonance |
EF | enhancement factor |
CNN | convolutional neural networks |
LSTM | long short-term memory |
AUC | area under curve |
PCA | principal component analysis |
LDA | linear discriminant analysis |
SAE | sparse autoencoder |
DNN | deep neural network |
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